Integrate LLM chat, RAG, and agent execution into R applications.
npx skills add https://github.com/arthurgailes/awesome-r-skills --skill r-ai
Please help me install this Agent Skill. Skill: r-ai Source: https://github.com/arthurgailes/awesome-r-skills/tree/main/skills/r-ai Command: npx skills add https://github.com/arthurgailes/awesome-r-skills --skill r-ai
This Skill simplifies the integration of Large Language Models (LLMs) into R workflows, enabling advanced AI capabilities like chat, Retrieval-Augmented Generation (RAG), and agent-based task execution directly within R.
Use the r-ai skill to chat with the OpenAI API using the provided API key.
High-intent search queries and answers about installing and using this skill.
To integrate LLM chat into an R application, use this Skill to connect with providers like OpenAI, Anthropic, and local Ollama models. You need to set the appropriate API keys and install R packages such as ellmer to enable direct chat workflows.
Yes, you can build a Retrieval-Augmented Generation (RAG) workflow in R to query your project documentation. This Skill supports document search and retrieval to ground LLM responses directly in your data using packages like ragnar.
To run AI agents locally, this Skill requires installing R packages like bttools, mcptools, and vitals. These packages facilitate agent-based task execution, allowing the agent to write and execute R code based on natural language instructions.
This Skill supports both cloud providers like OpenAI and Anthropic, as well as local Ollama models. You must configure the respective API keys for cloud providers, while local models run without external API dependencies.
You can evaluate the quality and accuracy of LLM outputs in R using the built-in evaluation features provided by this Skill. This allows you to assess generated responses, code execution results, and agent task accuracy directly within your R workflows.
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